Exploiting CPU SIMD Extensions to Speed-up
Document Scoring with Tree Ensembles
Short paper accepted at SIGIR ’16: ACM Conference on Research and Development in Information Retrieval .
Abstract. Scoring documents with learning-to-rank (LtR) models based on large ensembles of regression trees is currently deemed one of the best solutions to eﬀectively rank query results to be returned by large scale Information Retrieval systems.
This paper investigates the opportunities given by SIMD capabilities of modern CPUs to the end of eﬃciently evaluating regression trees ensembles. We propose V-QuickScorer (vQS), which exploits SIMD extensions to vectorize the document scoring, i.e., to perform the ensemble traversal by evaluating multiple documents simultaneously. We provide a comprehensive evaluation of vQS against the state of the art on three publicly available datasets. Experiments show that vQS provides speed-ups up to a factor of 3.2x.
The source code is available here: https://github.com/hpclab/vectorized-quickscorer.
 Claudio Lucchese, Franco Maria Nardini, Salvatore Orlando, Raﬀaele Perego, Nicola Tonellotto, and Rossano Venturini. Exploiting cpu simd extensions to speed-up document scoring with tree ensembles. In SIGIR ’16: Proceedings of the 39th International ACM SIGIR Conference on Research and Development in Information Retrieval, 2016.